from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def __lt__(     self						:						Optional[Any]   ,   lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    return self[-1] < other[-1]







   def __eq__(     self						:						int   ,   lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    return self[-1] == other[-1]





def 			lowercase(	UpperCamelCase_       )		->	list:







 '''simple docstring'''
 UpperCamelCase			  =      []
 # sort into stacks
 for element in collection:
  UpperCamelCase			  =      Stack([element]       )
  UpperCamelCase			  =      bisect_left(UpperCamelCase_							,					UpperCamelCase_       )
  if i != len(UpperCamelCase_       ):
   stacks[i].append(UpperCamelCase_       )
  else:
   stacks.append(UpperCamelCase_       )

    # use a heap-based merge to merge stack efficiently
 UpperCamelCase			  =      merge(*(reversed(UpperCamelCase_       ) for stack in stacks)       )
 return collection


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      input("""Enter numbers separated by a comma:\n""").strip()
     _SCREAMING_SNAKE_CASE 		=      [int(item) for item in user_input.split(""",""")]
     print(patience_sort(unsorted))

import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 # Initialise PyTorch model
 UpperCamelCase			  =      MobileBertConfig.from_json_file(UpperCamelCase_       )
 print(f"""Building PyTorch model from configuration: {config}"""       )
 UpperCamelCase			  =      MobileBertForPreTraining(UpperCamelCase_       )
 # Load weights from tf checkpoint
 UpperCamelCase			  =      load_tf_weights_in_mobilebert(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()							,					UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--mobilebert_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained MobileBERT model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
